Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Minimax and Hamiltonian dynamics of excitatory-inhibitory networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Stable local computation with conditional Gaussian distributions
Statistics and Computing
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
The Factored Frontier Algorithm for Approximate Inference in DBNs
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Variational Learning for Switching State-Space Models
Neural Computation
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Bayesian Gaussian Process Classification with the EM-EP Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Change Point Problems in Linear Dynamical Systems
The Journal of Machine Learning Research
Expectation Consistent Approximate Inference
The Journal of Machine Learning Research
Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
The Journal of Machine Learning Research
Learning and approximate inference in dynamic hierarchical models
Computational Statistics & Data Analysis
Bayes Machines for binary classification
Pattern Recognition Letters
Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models
Neural Information Processing
Monte Carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research
Multi-scale switching linear dynamical systems
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Journal of Computational Neuroscience
Network-based sparse Bayesian classification
Pattern Recognition
Sparse Spatio-temporal Gaussian processes with general likelihoods
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Robust Gaussian Process Regression with a Student-t Likelihood
The Journal of Machine Learning Research
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We describe expectation propagation for approximate inference in dynamic Bayesian networks as a natural extension of Pearl's exact belief propagation. Expectation propagation is a greedy algorithm, converges in many practical cases, but not always. We derive a double-loop algorithm, guaranteed to converge to a local minimum of a Bethe free energy. Furthermore, we show that stable fixed points of (damped) expectation propagation correspond to local minima of this free energy, but that the converse need not be the case. We illustrate the algorithms by applying t,hem to switching linear dynamical systems and discuss implications for approximate inference in general Bayesian networks.